Incremental Sparsification for Real-time Online Model Learning
Duy Nguyen–Tuong, Jan Peters ; JMLR W&CP 9:557-564, 2010.
Abstract
Online model learning in real-time is required by many applications, for example, robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component and cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The proposed approach combines a sparsification method based on an independency measure with a large scale database. In combination with an incremental learning approach such as sequential support vector regression, we obtain a regression method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real robot emphasizes the applicability of the proposed approach in real-time online model learning for real world systems.
